The Evolving Risk of Infective Endocarditis After Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
OBJECTIVES: Despite increased use of transcatheter aortic valve implantation (TAVI) in older adults with severe aortic stenosis, contemporary data on infective endocarditis (IE)-an infrequent but serious complication-are lacking. This study addresses this gap in knowledge. METHODS: We analysed 280 073 Medicare beneficiaries who underwent TAVI between 2013 and 2022. The primary outcome was the change in the 1-year incidence rate of IE post-TAVI. Joinpoint regression was used to evaluate the trend in the IE incidence as annual percent change (APC). Adjusted Cox models were used to evaluate associations between IE incidence and patient characteristics, as well as 12-month outcomes. RESULTS: The incidence rate of IE 1 year post-TAVI decreased from 20.0/1000 person-years in 2013 to 13.1/1000 in 2021. There was no change in incidence between 2013 and 2018 but a significant decline thereafter (-12.1% [CI, -20.7% to -7.5%], P < .001). This decline was associated with the decrease in non-elective TAVI (sub-distribution hazard ratio: 0.98 [CI, 0.94-0.99], P < .001); 4.8% of patients with IE underwent aortic valve reintervention. The 30-day aortic valve reintervention rate after IE increased significantly from 2013 to 2022 (APC: 24.9% [CI, 17.2%-33.0%], P < .001). The 30-day mortality rate after TAVI explant was 9.3%; the adjusted risk of death declined over time (HR: 0.73 [CI, 0.58-0.92], P = .01). However, the overall 30-day risk-adjusted mortality rate of TAVI-IE remained unchanged. CONCLUSIONS: The post-TAVI incidence of IE in Medicare patients decreased after 2019. This decrease was associated with declining rates of non-elective TAVI and coincided with FDA approval of TAVI for low-risk patients. TAVI explant rates were low but increased recently. The lack of improvement in 30-day mortality underscores the challenges of elderly care after TAVI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".